Knowledge Organisation in a Neonatal Jaundice Decision Support System

摘要

The tables containing the optimal decisions obtained when solving real decision-making problems under uncertainty are often extremely large. Tables can be considered as multidimensional matrices (MMs) and computers manage them as lists, where each position is a function of the order chosen (or base) for the matrix dimensions. In this paper, we propose turning the decision tables into minimum storage lists. Evolutionary computation is required to minimise the number of list entries (items). The optimal list includes the same knowledge as the original list, but it is compacted, which is very valuable for explaining expert reasoning. We illustrate the ideas using our decision support system IctNeo (Bielza et al., 2000) for neonatal management, outputting excellent results. The methodology is so general that it also applies to any table considered as a knowledge base (KB).